زينب عصام كنون
ABSTRACT
This thesis aims to find the optimal paths for multi-mobile robots working in the same static complex environment, as well as control their motion. To achieve this goal, three stages are proposed. In the first stage, a path planning method provides the shortest and smoothest path with collision avoidance between the starting and the target points in a static robot environment. This work proposes an intelligent hybrid optimization method that combines two algorithms; the first algorithm is called the Quarter Orbits (QO) algorithm, which tries to enhance the behavior of the cell decomposition path planning algorithm in terms of reducing the path length required to reach the target. The second algorithm uses the Particle Swarm Optimization (PSO) algorithm to get a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. The generated hybrid method is called the Quarter Orbits Particle Swarm Optimization (QOPSO). In the second stage, a proposed Inverse Differential Kinematic Neural Network Trajectory Tracking (IDKNNTT) controller based on a Modified Elman Recurrent Neural Network (MERNN) is used. This proposed controller is used to control the nonlinear kinematics mobile robots’ system to smoothly and quickly generate the left and right wheels’ velocities of the multimobile robots, which are used to control the orientation and position of each mobile robot. In particular, the controller guarantees that all the mobile robots will follow their desired paths quickly and correctly. Moreover, at the third stage, an adaptive PID-PSO low-level controller is proposed to control the left and right wheels velocities of the DC motor with minimum velocity error to guarantee that each mobile robot follows its desired path and to design this controller a programmable integrated circuit (IC) will be used. This IC was implemented using a field programmable gate array (FPGA) board Spartan-3E kit. All simulation results clearly show that the proposed hybrid path-planning algorithm provided the shortest path with collision avoidance. The proposed hybrid QOPSO algorithm achieves enhancement on the path length equal to 29.42% compared to the VCD algorithm and 24.25% compared to the RCD algorithm for different types of simple and complex maps. To validate the numerical simulation results of the proposed control strategy they were compared to those of other types of controllers in terms of the maximum error enhancement in the X-position and the Y-position. Particularly, when the proposed controller was compared to the Convolutional Neural Network Trajectory Tracking (CNNTT) controller, the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 75.5% and enhances the tracking error rate on the Y-axis by 21.2%. In addition, the proposed controller was compared to the MIMO-PID-MENN controller, and the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 33.3% and on the Y-axis by 40.6%. Moreover, the simulation and emulation result of using the low-level FPGA-PID-PSO controller provides a minimum velocity error of approximately zero value.
